The proposed deep Multi-resolution Dictionary Learning (deep MRDL) model is an extension of the work by Zhang et al. [1]} and is built on top of the Deep TEN: Texture Encoding Network. It uses the "Encoding-Layer" (gray boxes in Figure REF ) which consists of four blocks: dictionary, residuals, assignment weights, and aggregation. It is a novel layer integrated on top of any desired convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. The main advantage of deep TEN compared to the previous deep dictionary learning approaches is that it is an end-to-end learning framework, where the inherent visual vocabularies are learned directly from the loss function. The features, dictionaries, encoding representation and the classifier are all learned simultaneously (gradient information passing to each component during back propagation, tuning each component). The Encoding Layer has three main properties. (1) The representation is orderless and describes the feature distribution, which is suitable for material and texture recognition. (2) The Encoding Layer acts as a pooling layer integrated on top of convolutional layers, accepting arbitrary input sizes and providing output as a fixed-length representation. By allowing arbitrary size images, the Encoding Layer makes the deep learning framework more flexible. And, (3) the Encoding Layer learns an inherent dictionary and the encoding representation which is likely to carry domain-specific information and therefore is suitable for transferring pre-trained features.
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